Driver measurement and incentive system for improving fuel-efficiency

ABSTRACT

A vehicle driver is provided with a display interface a smartphone, tablet, PC, or any telematics or in-vehicle device installed in the vehicle. The display interface presents a real-time target for the driver to follow to maximize fuel economy and safety, achieved by modulating the accelerator pedal appropriately.

BACKGROUND

1. Technical Field

This disclosure relates generally to applications and methods that maybe implemented in or using a mobile device to teach, measure and rewardmotor vehicle drivers to operate their vehicles in a fuel-efficient andsafe manner.

2. Background of the Related Art

Mobile devices, such as a smartphone or tablet, have become ubiquitousin today's society. Faster processors, more memory, higher qualitygesture-based multi-touch screens, availability of mobile broadbanddata, and integration of multi-media and GPS chips along with openinterface mobile operating systems, have opened the door for creation ofa large variety of mobile applications.

With the price of fuel hovering around $4 per gallon in the US, fuel hasgrown to be one of the largest costs of running a vehicle. Mostcommercial fleets consider fuel to be over 35% of operating expenses. Asfuel prices continue to increase, this challenge becomes even greater.Commercial fleets are most competitive when they are best at overallfreight efficiency, which is largely enabled by managing fuel cost.

Alternative vehicle technologies (truck-level features to improveaerodynamics, decrease rolling resistance, C/L NG engines andhybrid/electric drivetrains) are currently offered to fleet owners andmanagers to help control the fuel costs. These solutions are expensivebut somewhat effective in increasing fuel mileage and therefore loweringfuel costs. These capital intensive solutions drive a challenging returnon investment (ROI) and an extended payback period to the fleetoperator. Furthermore, these solutions are ignoring a potentiallysimpler opportunity, namely, changing driver behavior to improve fuelmileage. Fleets and OEM's agree on a difference of more than 30% in fuelmileage between a fleet's best and worst driver with respect to MPG.Furthermore, drivers with low MPG's tend to have a higher accident rate.

Additionally, and another challenge, is that the profession of truckdriving is rife with high turnover, as many drivers opt to move to a newfleet once a year or more, chasing a better route, better pay, orimproved lifestyle.

Of course, the challenge of changing driver behavior to improveefficiency very likely pre-dates the internal combustion engine.Highly-skilled “teamsters” managed draft animals to maximize freightefficiency. Since those days, there have been primarily two toolsutilized to improve driver behavior and efficiency, namely, educationand incentive programs. For example, many fleets send all drivers totraining on a regular basis to improve techniques for efficient driving.Others offer fuel mileage or other bonuses for good behavior andresults. Both programs find limited success. Education and trainingprograms are costly and the benefits tend to wane over time, or driversmove on to a different fleet; incentives and bonuses tend to reward thetop tier of drivers, and if a driver knows he/she will not receive it,they refuse to try.

Most modern trucks have a fuel mileage gauge in-dash. The mostsophisticated of these gauges report instantaneous mpg, average mpg, andtrending details in some graphical format. These gauges are somewhateffective in improving a driver's fuel mileage, however, they are bynature hind-ward looking and are generally providing negative feedback.For example, when the driver begins a journey, the fuel economy value isoften as “good as it will get” and declines as the driver is faced withexternalities beyond his or her control (i.e. traffic jams, rerouting,truck maintenance issues, hills, etc). This is frustrating anddiscouraging to the driver, and human tendency in these instances is todisregard, turn-off, or look away from the gauge. Furthermore, theindustry generally recognizes a 15-20% error in reading the fuel mileagefrom the Engine Control Unit. Socha significant inaccuracy makes itdifficult to know if the driver is doing better or worse than theprevious journey, mile, or minute. In-dash and other MPG gauges use ECUcalculation of mileage and are therefore neither accurate nor precise.

Since the advent of cellular and mobile technologies, the “telematics”industry has evolved into a mature market with many solutions and keyplayers (Qualcomm, PeopleNet, Telogis, Xata, and others). Theirsolutions primarily offer truck-specific GPS routing, navigation,compliance with FMCSA and NHTSA legislation (such as Hours of Service,Electronic On-Board Recording, and Compliance, Safety and Accountability(CSA)) and back-office integration with accounting, payroll, and fueltax reporting. These incumbent technologies are just now beginning toextend their solutions to help “change driver behavior to improve fueleconomy”. As of today, their driver behavior solutions are limited toanti-speeding and anti-idling programs, with a monthly driver scorecard.For example, they set targets (no speed violations, X% of time spentidling), measure, and report to the driver their performance on aperiodic basis (usually monthly).

There also exists a solution targeting improving driver safety, known asGreenRoad. This is a dash-mounted device that uses an array ofaccelerometers, a red-yellow-green light and a mobile data connection.When a driver maneuver exceeds a certain threshold as measured by theaccelerometers, the light changes, from green to yellow, and then tored, in a progressive fashion. This real-time feedback attempts to limithard acceleration/deceleration and hard cornering, which have beendirectly correlated to increased rate of safety incidents. GreenRoad hasalso recognized that limiting hard acceleration and deceleration eventsleads to improved fuel mileage. The fleets have access to an onlinereporting system that can be used by the fleet manager to train drivers.

BRIEF SUMMARY

According to an embodiment, computer program code is executed by a hostcomputing device (e.g., a mobile device) having a display for thepurpose of coaching an operator of a vehicle to operate the vehicle in afuel-efficient manner. The program code is operative to generate adisplay interface (e.g., a simulation of a gauge) identifying two ormore target zones, and a display pointer. The target zones are definedby a baseline that represents a set of zero acceleration fuel rateversus vehicle speed data points, the baseline being generated for agiven vehicle class of which the vehicle is a member. The baseline maybe adjusted based on environmental data (e.g., time of day, temperature,hill detection, altitude, current weather, route grade changes fromtopological maps, and the like) and/or historical data (e.g., driverhistory, vehicle history, or the like). The program code is furtheroperative to receive data (e.g., vehicle speed, engine load, enginespeed, fuel rate, transmission gear value, mass air flow, and the like)generated during operation of the vehicle; in response, the program codeis further operative to generate, continuously, values representing acurrent fuel rate. The program code converts these values to controlsignals that drive the display pointer with respect to the two or moretarget zones to provide visual feedback of current fuel rate against thebaseline. The visual feedback seeks to force the operator to operate thevehicle in such a manner so as to maintain the display pointer in agiven position with respect to the baseline, thereby increasing theoverall fuel-efficiency.

The foregoing has outlined some of the more pertinent features of thesubject matter. These features should be construed to be merelyillustrative.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the disclosed subject matter andthe advantages thereof, reference is now made to the followingdescriptions taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is a block diagram of a driver measurement and incentive serviceprovider infrastructure according to this disclosure;

FIG. 2 is a representative client-side (mobile) device that executes anin-vehicle application for teaching, measuring and rewarding a vehicledriver according to this disclosure;

FIG. 3 is a process flow of a driver measurement and incentive processfor improving vehicle fuel-efficiency according to the techniquesherein;

FIG. 4 illustrates a representative client-side user interface (UI) on amobile device display according to this disclosure;

FIG. 5 illustrates a set of software-based services that execute in thecoach application of this disclosure; and

FIG. 6 illustrates how various algorithms may be used to calculatetarget variables (e.g., fuel rate, air mass, or the like) according tothis disclosure.

DETAILED DESCRIPTION

The disclosed method may be practiced in association with a computinginfrastructure comprising one or more data processing machines.

A representative computing infrastructure provides a driver measurementand incentive service for improving fuel-efficiency. A representativeservice of this type is PedalCoach™ provided by LinkeDrive of Boston,Mass. This type of service (in whole or in part) may be implemented onor in association with a service provider infrastructure 100 such asseen in FIG. 1. A representative infrastructure of this type comprisesan IP switch 102, a set of one or more web server machines 104, a set ofone more application server machines 106, a database management system108, and a set of one or more administration server machines 110.Without meant to be limiting, a representative technology platform thatimplements the service comprises machines, systems, sub-systems,applications, databases, interfaces and other computing andtelecommunications resources. A representative web server machinecomprises commodity hardware (e.g., Intel-based), an operating systemsuch as Linux, and a web server such as Apache 2.x (or higher). Arepresentative application server machine comprises commodity hardware,Linux, and an application server such as WebLogic 9.2 (or later). Thedatabase management system may be implemented as an Oracle (orequivalent) database management package running on Linux. Theinfrastructure may include a name service, FTP servers, administrativeservers, data collection services, management and reporting servers,other backend servers, load balancing appliances, other switches, andthe like. Each machine typically comprises sufficient disk and memory,as well as input and output devices. The software environment on eachmachine includes a Java virtual machine (JVM) if control programs arewritten in Java. Generally, the web servers handle incoming requests,and they export web pages (or the like) or other content. Theapplication servers manage the basic functions of the service including,without limitation, business logic.

One or more functions of such a technology platform may be implementedin a cloud-based architecture. As is well-known, cloud computing is amodel of service delivery for enabling on-demand network access to ashared pool of configurable computing resources (e.g. networks, networkbandwidth, servers, processing, memory, storage, applications, virtualmachines, and services) that can be rapidly provisioned and releasedwith minimal management effort or interaction with a provider of theservice. Available services models that may be leveraged in whole or inpart include: Software as a Service (SaaS) (the provider's applicationsrunning on cloud infrastructure); Platform as a service (PaaS) (thecustomer deploys applications that may be created using provider toolsonto the cloud infrastructure); Infrastructure as a Service (IaaS)(customer provisions its own processing, storage, networks and othercomputing resources and can deploy and run operating systems andapplications).

The platform may comprise co-located hardware and software resources, orresources that are physically, logically, virtually and/orgeographically distinct. Communication networks used to communicate toand from the platform services may be packet-based, non-packet based,and secure or non-secure, or some combination thereof.

More generally, the techniques described herein are provided using a setof one or more computing-related entities (systems, machines, processes,programs, libraries, functions, or the like) that together facilitate orprovide the described functionality described above. In a typicalimplementation, a representative machine on which the software executescomprises commodity hardware, an operating system, an applicationruntime environment, and a set of applications or processes andassociated data, that provide the functionality of a given system orsubsystem. As described, the functionality may be implemented in astandalone machine, or across a distributed set of machines.

The front-end of the above-described infrastructure is alsorepresentative of a conventional network-accessible web site or webservice. This site or service provides a software application thatexecutes in a mobile device to provide the in-vehicle “coaching”functionality of this disclosure, as will be described below.

More generally, client (vehicle driver-side) devices access the serviceprovider infrastructure via a network (e.g., the public Internet, aprivate or dedicated network, or any combination) to provide data, andto retrieve content, including HTML, media players, video content, andother objects. A typical client device is a personal computer, laptop,mobile device, tablet, or the like. A representative mobile device is anApple iPad® or iPad2, iPad Mini, an Android™-based smartphone or tablet,a Windows®-based smartphone or tablet, or the like. A device of thistype typically comprises a CPU (central processing unit), computermemory, such as RAM, and a flash drive. The device software includes anoperating system, and generic support applications and utilities.

A representative mobile device is shown in FIG. 2. The device 200comprises a CPU (central processing unit) 202, such as any Intel- orAMD-based chip, computer memory 204, such as RAM, and a drive 206. Thedevice software includes an operating system (e.g., Apple iOS, Google®Android™, or the like) 208, and generic support applications andutilities 3210. The device may also include a graphics processing unit(GPU) 212. In particular, the mobile device also includes atouch-sensing device or interface 214 configured to receive input from auser's touch and to send this information to processor 212. Thetouch-sensing device typically is a touch screen. The touch-sensingdevice or interface 214 recognizes touches, as well as the position,motion and magnitude of touches on a touch sensitive surface (gestures).In operation, the touch-sensing device detects and reports the touchesto the processor 212, which then interpret the touches in accordancewith its programming. The mobile device typically also includes otherinput/output devices include software-based keyboards, cameras,microphones, and the like.

More generally, the mobile device is any wireless client device, e.g., acellphone, pager, a personal digital assistant (PDA, e.g., with GPRSNIC), a mobile computer with a smartphone client, or the like. Typicalwireless protocols are: WiFi, GSM/GPRS, CDMA or WiMax. These protocolsimplement the ISO/OSI Physical and Data Link layers (Layers 1 & 2) uponwhich a traditional networking stack is built, complete with IP, TCP,SSL/TLS and HTTP.

Thus, a mobile device as used herein is a 3G—(or next generation)compliant device that includes a subscriber identity module (SIM), whichis a smart card that carries subscriber-specific information, mobileequipment (e.g., radio and associated signal processing devices), aman-machine interface (MMI), and one or more interfaces to externaldevices. The techniques disclosed herein are not limited for use with amobile device that uses a particular access protocol. The mobile devicetypically also has support for wireless local area network (WLAN)technologies, such as Wi-Fi. WLAN is based on IEEE 802.11 standards.

The client is not limited to a mobile device, as it may be aconventional laptop or other Internet-accessible machine running a webbrowser or mobile application (app). Content retrieved to the client maybe rendered in a browser, within a mobile app, or other renderingengine.

The client may also be a telematics device installed in the vehicle.

The client also may be integrated into existing in-vehicle devices, suchas a fuel mileage gauge in-dash.

Driver Measurement and Incentive System for Improving Fuel-Efficiency

With the above-described enabling technologies, the techniques of thisdisclosure are now described.

To address the above-described prior art deficiencies, and according tothis disclosure, a vehicle driver is provided with a display interface(e.g., a graphical user interface (GUI)) via the smartphone, tablet, PC,or any telematics or in-vehicle device installed in the vehicle. As willbe described, the user interface preferably presents a real-time targetto follow to maximize fuel economy and safety, achieved by modulatingthe accelerator pedal appropriately. Preferably, this target is derivedvia algorithmic techniques (a set of one or more algorithms) that usedata from the following one or more input sources: Engine Control Unit(ECU) data including, without limitation, vehicle speed, engine load,engine speed, fuel rate, mass air flow, and the like, third-partysupplied data such as temperature, % grade, time of day, and the like,and other (potentially proprietary) data such as driver history, vehiclehistory, location target, and the like. The one or more algorithmspreferably execute in an application running in the mobile device (orother telematics or i-vehicle device). In a preferred embodiment, theapplication runs on a web-connected mobile device, connected to a motorvehicle's engine control unit (ECU), either wirelessly or via cable(e.g., Ethernet, Firewire, or the like). Using the various data sourcesdescribed, e.g., data received via the Internet or otherwise (i.e. %grade, driver ID, proprietary data, temperature, and the like), datafrom the mobile device (i.e. GPS location, time of day, or other data),and data from the ECU (mass air flow, fuel rate, rpm, vehicle speed, orother data), a display is rendered by the device to teach a driver howto move the vehicle in the most efficient and safest manner.

Generalizing, there may be several data sources that may be used todrive the application. These include: first data, which originates inthe vehicle itself; second data, which originates externally from thevehicle and represents one or more local (to the vehicle) environmentalcondition(s) associated with a current operation (real-time, or nearreal-time) of the vehicle; and third data that is historical in natureand that associates one or more of the following: this particulardriver, this particular vehicle, this particular job, and thisparticular route. The first data is typically derived from the vehicleECU system but in general may be any data that originates in the vehicleaccording to one or more of the following industry standards: SAE(Society of Automotive Engineers International) J1939, SAE J1708, SAEJ1587, and SAE J1979. The second data typically is one of: time,temperature, percent grade, wind conditions, weather, altitude (fromGPS), hill detection, forward looks at the route grade from topologicalmaps, and the like. The third data typically is specific to the driver,vehicle, job and/or route for which the calculation is being generated.The third data may not always be available; when third data isavailable, it is often useful to seed the calculations, as will bedescribed.

As will be described, an underlying premise of the technique herein isthat fuel rate can be approximated as linearly increasing as the vehiclespeed increases with all other factors constant. To generate this lineof fuel rates versus speeds, events that fall around an accelerationwindow (typically zero) are collected and used to determine a fittedfirst order curve. These collected events can be used to generate anassumption of a fuel rate at a predetermined higher speed (typically 65miles/hour or its km/hour equivalent). Using one or more of thebelow-described algorithms, this fuel rate is then used to set a fuelrate limit over a full range of vehicle speeds. As more data iscollected, this fuel rate is further refined and updated. Initially,this fuel rate is seeded through remote/local database lookups, and itis refined as more data is collected and confidence of the collecteddata is high (or at or above a configurable threshold). When externaldata sources are available, the fuel rate is permitted either to rise orfall as a function, for example, of one or more of: grade %, wind speedand direction, vehicle load, driver habits, and typical route speeds andfuel rates for vehicles of the same type.

Preferably, one or more algorithms as described below operate to atarget (fuel rate <diesel> or air mass <spark ignition>) for the driverto follow which varies, for example, according to the difficulty of themission of the vehicle. Furthermore, preferably a score is presented tothe driver such that performance can be measured. In another aspect, anindicator is provided in the form of a display pointer relative to otherdisplay indicia that guide or coach the driver regarding the operatingcharacteristics (e.g., an amount of throttle) to apply as the vehiclemoves along the route. When utilized, the application improves fuelmileage, reduces safety incidents, and helps fleets retain their bestdrivers, e.g., by facilitating incentive programs. For example, usingthe application, the driver is scored and earns or loses “points” basedupon their performance against the target. On a periodic basis, a scoreis used to offer the driver an incentive such as a pre-paid credit card.

FIG. 3 illustrates the basic techniques of this disclosure, as have beendescribed above. Typically, there are a set of mobile device-specificoperations, and the mobile device may interface to a service providerinfrastructure as necessary. As noted above, the display interfacepreferably is generated by a software application that provides thedescribed functionality (on the driver side), typically using localdisplay resources in the mobile device (depending on the type of deviceused). The application may be provided by the service provider, by athird party (e.g., an app store), or it may be integrated (or native to,as original-equipment) with an in-vehicle device.

The following describes the basic operating scenario. Typically, thevehicle is a truck driven by an operator (a driver). This is not alimitation, however, as the techniques herein may be implemented in anytype of vehicle (including passenger automobiles, boats, aircraft orother machines) whose fuel efficiency may be monitored and in whichvisual feedback may be provided to an operator of the vehicle duringoperation. The vehicle may be being driven remotely by an operator, orthe technique may be implemented in a “simulated” driving environment(such as in conjunction with a simulator or training device or system).The techniques herein may also be implemented as a training system ortool within a machine or system, e.g., using test or simulated data forthe various inputs, to train vehicle operators for when they get out inreal-world situations.

For convenience, the application described herein is sometimes referredto as a “coach” or “coaching” application, as it is used to train theoperator to use the vehicle pedal more efficiently, thereby improvingoverall vehicle fuel economy.

In the typical in-vehicle operating scenario, and with reference to FIG.3, the operator starts the vehicle (at step 300), which activates thecoach application. The vehicle is then operated (step 302) with theassistance of the coaching application (as will be described), whichapplication considers the driving mission and operating conditions,traffic conditions and other data, and provides visual and/or auralfeedback to the driver. The vehicle is operated under load on a road.When the drive ends, the vehicle is turned off, and the application isclosed (step 304). While in motion, various types of vehicle data suchas speed, engine RPM, fuel rate, and the like, are collected (step 306).As noted above, typically this is data from the vehicle electronic unit(ECU) and includes vehicle speed, engine speed, engine load, percenttorque, fuel rate, air mass, and the like. This data is pushed to theon-board device (e.g., via wireless or cable) (at step 308). The coachapplication 305 executes as software (a set of computer programinstructions) in the mobile device and performs a number of high leveloperations as depicted. Its primary functions are calculating a targetfuel rate based on various data inputs (at step 310), and using theoutput of this calculation to control rendering of a new target value ona display interface (at step 312). The target fuel rate (or, in thealternative, air mass) calculation preferably also uses GPS datacollected from the mobile device network (at step 314). Typically, datafrom the device (e.g., GPS location) is pushed to the applicationwirelessly. The output of the calculation may also be used to generate“points” for a driver who complies with the target fuel rate (at step316).

The service provider, which may be cloud-based as shown, provides andreceives various data, and it provides one or more services. Thus, forexample, typically the data from the ECU, as well as the output(s)generated by step 310, are provided to the service provider database(e.g., via mobile data connection) (at step 318). Driver performancedriver data captured by the coach application is logged to the serviceprovider (at step 320). Historical data and updates as needed aredelivered to the coach application (at step 322). For example, theservice provider database typically pushes various types of data, e.g.,driver history, driver handicap, location difficulty, vehicle handicap,% grade, time of day, traffic, temperature, and the like, to theapplication. The service provider preferably also executes a cloud-baseddriver management process and its associated database operations (at324), which provides driver incentives or other feedback (at step 326).

In one embodiment, the calculation performed at step 310 works generallyas follows. In this example scenario, the calculation is executedon-board the mobile device (and, in particular, the coach applicationexecuting thereon) runs a routine against all (or some subset of) inputsto determine what the target (e.g., fuel rate, air mass, and the like)should be for this particular driver, this route, this load, and thisvehicle. In the alternative, the calculation (or some portion of it) maybe executed in the service provider environment, or elsewhere. Thedevice then receives the target value (or values). Using a displayinterface, the target value is then rendered, preferably in a graphicalmanner.

FIG. 4 illustrates a representative display interface 400 for thispurpose. The interface may be generated on the mobile device by thecoach application, or the outputs from that application may interface toanother display application. The display interface 400 is illustrated asa gauge (to be consistent to a standard in-vehicle display format) thatincludes a set of spaced values as shown. Preferably, the gauge issemi-circular and includes three (3) or more zones, such as green 402,yellow 404 and red 406. A pointer 405 is driver around the gauge basedon the results of the calculation (step 310 in FIG. 3). The displaypreferably is updated continuously, periodically (e.g., every fewseconds), or in some combination thereof. There may be particulardriving conditions during which the operation of the display issuspended. Additional operator feedback is provided by a set of lights408, 410 and 412 (e.g., simulated LEDs), which illuminate green, yellowand red, respectively. The interface also preferably includes a runningpoint meter 414, whose value increases provided the driver maintains thepointer within some acceptable range.

Thus, according to this disclosure, data regarding a specific vehicleclass (type) is collected and fuel rate versus vehicle speed evaluated(using some empirical data analysis technique). If other data (e.g.,information about the particular driver, specific information about theactual vehicle itself, or the like) is available, that other data may beevaluated as well during this process. During this evaluation,preferably zero acceleration events are filtered out. The zeroacceleration events correspond to events when the vehicle is notaccelerating or decelerating but, rather, travels at constant speed.While there may be lots of small differences over short periods of time,over a large chunk of data these differences tend to average out and theresulting data points represent a function (in effect, “how much fueldoes it take to keep the vehicle at a steady speed”). These zeroacceleration fuel rate versus vehicle speed data points form a line. Theline is described by y=mx+b, where x=speed, and y=fuel rate. This linerepresents the amount of fuel needed to keep this type of vehicle atspeed assuming ideal conditions (no acceleration, driving on a flat andsmooth surface, with no wind, a constant load, etc.). This becomes a“baseline” for the vehicle class. Ideally, any fuel rate below this linefor a given speed represents “green” on the display interface gauge. Anyfuel rate above this line for a given speed represents “yellow and red”on the gauge. If this optimal rate is held, the resulting curverepresents the “green/yellow” transition point for the gauge.Preferably, the application gives drivers a little more room at lowerspeeds than what is represented on the curve. As an example of thisapproach, a speed point (e.g., 65 MPH) may be selected and the fuel ratecapped with the corresponding fuel rate from the baseline. If the driveruses no more than this fuel rate, the vehicle should be able to achievea gradual acceleration to that speed and then be able to hold it. Ifmore fuel than that is used, the pointer enters the yellow or red zones.Preferably, the calculations may adjust dynamically for events oroccurrences (e.g., hills, head winds, or the like) beyond the driver'scontrol. As additional statistically-relevant fuel rate and speed datapoints (around zero acceleration) are available, the baseline may berecalculated. Typically, it will be desirable to adjust the baselinebased on external data (e.g., hill detection, altitude from GPS, forwardlooks at the route grade from topological maps) and, if available andstatistically-significant, historical information (e.g., about thedriver, the vehicle, the driver's past history in the vehicle for theparticular route, and so forth).

The display pointer in the gauge may be scaled, e.g., by adjusting aweight to be applied to the difference of the actual fuel rate and therecommended/ideal fuel rate and that comparison to a position of thepointer for feedback to the operator. The display pointer may also bescaled to provide different degrees of difficulty based on a proficiencyof the driver; thus, as the driver becomes more proficient, it may bedesirable to scale the pointer to increase the difficulty (ofmaintaining the pointer in the green zone) so that the driver's skillsmay be further improved using the coaching technique.

The display interface preferably is rendered by the coach application.In an alternative, the gauge, lights and point meter may be virtual(e.g., projected via a heads-up display, Google Glasses™, or the like.

The visual cues may be supplemented (or even replaced) with audiblecues, tactile (haptic-based) cues, or some combination(s) thereof. Thus,for example, the mobile device may buzz when the pointer moves out ofthe green zone, or a signal may be sent to a haptic device embedded inthe steering wheel, or the driver's seat, to provide a vibration. Ofcourse, these are merely examples.

The display interface may use other display formats and constructs(e.g., linear scales, numerical read-outs, and the like) in lieu of (orto supplement) the gauge.

The display may be augmented to include other information that may beused in the target calculation.

The application may be configured to present statistics or other reportsto the driver upon given occurrences, such as at key-off. Thus, forexample, the application may be configured to provide a driver summaryand a driver “leaderboard” so that the driver can determine his or hercurrent status with respect to other drivers.

FIG. 5 illustrates a set of software-based services that execute on themobile device and comprise the coach application. These include avehicle ECU communication service 500, a data processing and algorithmservice 502, a data posting service 504, and a display service 506. ECUcommunication service provides the vehicle data for the calculation. Thedata processing and algorithm service 502 performs the calculations togenerate the target data, using data supplied by the ECU communicationservice 500, as well as data provided from the remote databases andstorage and forwarded through the data posting service 504. Data postingservice 504 also collects driver performance data and posts it to theservice provider databases. The display service 506 takes the outputsgenerated by the service 502 and uses them to drive the displays, in themanner described.

In operation, the target is calculated (based on the inputs), driverperformance against the target is measured, points are accumulated, andscoring presented. The service database logs performance data by thevehicle, by the operator, and by location. Preferably, an incentiveprogram is offered to the driver based upon achievement of a minimumscore, e.g., as established by a fleet manager.

To use the system in the vehicle, the driver turns on the mobile deviceand starts the vehicle. A wireless link is established. The driverpreferably drives so as to keep the needle in the “green” zone. Pointsare then awarded, e.g., based on miles driven in the green zone. Milesdriven in the yellow zone may be deemed neutral. However, any excursionsinto the “red” zone preferably lock-out the point-logging for a giventime period (e.g., 10 seconds). As indicated, preferably the score ispresented as a percentage, calculated as a number of points per numberof miles. The score at the end of a measurement period may then resultin an incentive bonus based on the fleet and driver targets. Theapplication is turned off as needed or desired.

The subject matter therein thus provides for a software applicationintended to run on a network-connected mobile device, and connected toreceive data from a motor vehicle's engine control unit (ECU). Using thedescribed data sources (or some subset of them), a target value isgenerated and an indication is rendered by the device to teach a driverhow to move the vehicle in a most-efficient manner. In operation, acalculation executed by the application creates a target (fuel rate<diesel> or air mass <spark ignition>) for the driver to follow, andthat target preferably varies according to the difficulty of the missionof the vehicle. Furthermore, a score is presented to the driver suchthat performance can be measured. Financial or other rewards may beoffered to the driver periodically on the basis of these scores. Anytype of driver incentive program may be used.

The following provides additional details regarding the calculationsthat are used to drive the display interface. According to thisdisclosure, a set of algorithms are used for this purpose. As notedabove, an underlying assumption is that the fuel rate to maintain avehicle at a constant speed, with all other factors (grade %, windspeed, load) constant, should linearly increase as speed increases. Eachalgorithm typically, without limitation, takes in information from thefollowing sources: vehicle Engine Control Unit (ECU) (namely, one ormore of: vehicle speed, fuel rate, engine speed, transmission gearvalue, brake switch, vehicle identification, and component information),the mobile device itself (e.g., GPS Location, GPS Altitude,accelerometer values, and time/date), and external data sources (e.g.,temperature, grade %, vehicle information, driver history, and priorfuel limits). As noted above, the ECU data is gathered usingcommunication methods as defined in the industry standard documentswhich include, but is not limited to: SAE (Society of AutomotiveEngineers International) J1939, SAE J1708, SAE J1587, and SAE J1979.Preferably, outputs returned from each algorithm are an instantaneousfeedback, typically in the form of a numerical value from 0 to 100%(needle position of the pointer 405) that will reflect a driver'sability to maintain his or her fuel rate (an amount of fuel used perunit time) in an acceptable range determined by the algorithm's fuelrate limit. Along with this feedback, the driver preferably alsoreceives a number of points based upon distance travelled and needleposition reference previously. These points are then used as part of theincentive program.

Preferably, there are four (4) distinct algorithms that may be used invarious ways, as will be explained. These algorithms are referred toherein for convenience as SB, P, G and B. Generally, some combination ofthe algorithms is used to develop a fuel rate limit based upon thevehicle, driver, payload and driving environment.

Initial (or default) fuel rate limits, prior to acquiring an adequatedata set, may be derived using a local and remote database lookup basedupon prior vehicle history using vehicle ECU information including VIN,serial number, model, make, and the like. If this information isunavailable or has not been delivered (by the Vehicle ECU) to the mobiledevice, logged-in fleet or logged-in driver information may be used togenerate fuel rate limits. This is considered the initial seeding,whereby the fuel limit is set on those criteria and then further refinedusing the algorithms listed below. Some “softening” of the fuel limitmay be factored into each limit based upon changing short termenvironmental conditions, e.g. headwind, crosswind, grade changes.During these events, the application typically allows the driver tomaintain his or her current speed or maintain current fuel rate, buttypically discourages the driver to accelerate during the events. Ofcourse, the application may always make exceptions in the event ofhazardous conditions.

The SB algorithm generates a first order polynomial fitted curve basedupon fuel rate and vehicle speed based on acceleration value andtransmission gear value. This curve is then used to determine themaximum allowable fuel rate at a given speed. The fuel rate limit can beadjusted based upon a difficulty set for the driver. This difficultyadjusts the zero speed intercept of the fuel limit curve. In a preferredembodiment, this algorithm thus takes in driver, company, ECU vehicleinformation (e.g., VIN, make, and model) to determine a fuel limit forthe driver based upon a database query. It then takes in vehicle speedand fuel rate to generate a needle position for the display gauge, andto collect driver points earned, as well as possible driver points.

The P algorithm determines the maximum allowable acceleration that willnot increase trip duration, e.g., based upon the EPA's Federal TestProcedures (FTP) Drive Cycle. This calculation leads to an allowableacceleration value over the speed range of the vehicle. Fuel rate limitsare then developed by vehicle speed, fuel rate, engine speed andtransmission gear value, preferably to generate a simple regression ofthe vehicle speed and fuel rate. These limits are then used to generatethe three (3) distinct driver feedback zones of fuel rate (green, yellowand red). In this algorithm, the fuel rate limits are allowed to changebased upon the statistical deviation from previously-collected data tothe current collected data. In a preferred embodiment, this algorithmtakes in time, vehicle speed, and fuel consumption to determine a fuellimit based upon the acceleration of the vehicle. It then outputs aneedle position for the display gauge, and to collect driver pointsearned, as well as possible driver points.

The G algorithm stores instantaneous events containing vehicle speed,fuel rate and engine speed to finite sized bins based upon theiracceleration and transmission gear value. When a statistically-relevantnumber of bins are filled, a (least-squares) linear regression isdeveloped using the vehicle speed and fuel rate. This regression is thenused to determine the top speed fuel rate, which is used to generate afuel rate limit. Preferably, these fuel rate limits are continuouslyupdated based upon incoming data and drop out of older, less relevantdata samples. In a preferred embodiment, this algorithm takes takes intime, vehicle speed, and fuel consumption to determine a fuel limitbased upon acceleration of the vehicle. It then outputs a needleposition for the display gauge, and to collect driver points earned, aswell as possible driver points.

The B algorithm calculates Δspeed/Δfuel rate values based upon theinstantaneous events of the vehicle with reference to the currentacceleration and transmission gear value. The Δspeed/Δfuel rate valuepreferably is then cross-referenced to a look up table, either locallyor remotely, that stores fuel rate limit curves based upon empiricaldata analysis of optimum fuel rates for similarly derived Δspeed/Δfuelrate values. During a typical drive, this number typically updates basedupon varying environmental conditions, e.g. change in payload, gradechange, and wind direction, which will then reference the lookup tableto acquire a more appropriate fuel rate limit. In a preferredembodiment, this algorithm takes in time, vehicle speed, and fuelconsumption to determine a slope of fuel consumption and vehicle speed,which is then used to determine a fuel limit based upon a databasequery. It then outputs a needle position for the display gauge, and tocollect driver points earned, as well as possible driver points.

A combination of the described algorithms, or any of them individually,may be used to define the instantaneous fuel limit of the vehicle.

A combination of these algorithms preferably is used to generate theoptimum fuel rate limit for the vehicle at any given time. FIG. 6illustrates representative permutations of the algorithms. Asillustrated, in a first embodiment, as represented at 602, algorithm SBis executed initially individually, or more typically, in associationwith one or more of the other algorithms as shown. In a secondembodiment, as represented at 604, algorithm G is executed initiallyindividually, or in association with the SB or P algorithms. A thirdembodiment, as represented at 606, involves algorithm B being executedinitially, either individually or followed by one or more of thealgorithms shown. A fourth embodiment, as represented at 606, involvesexecuting algorithm P initially, either by itself or in association withone of the B and SB algorithms. Thus, as indicated, each of thealgorithms may be executed either alone or in some combination.Regardless of which algorithm(s) are used, typically the initialseedings (and thereafter updates) come from the vehicle ECU or from datareceived to the application from remote data sources, as has beendescribed. The one or more algorithms are executed to further andcontinuously refine the target (fuel rate or air mass) limit that thendrives the pointer and other display elements.

The disclosed subject matter provides significant advantages. Bydeploying the coach application, vehicle fleets radically improveoperating costs by lowering overall fuel consumption, reducing insurancepremiums, and improving driver retention. The coach application easilyintegrates with existing smartphone, tablet, and telematics solutions.The described approach is readily implemented so little or no additionalworkflow is required for a fleet to get started and begin saving.

The coach application provides an in-vehicle cab, real-time user displayof fuel mileage performance, thereby enabling immediate coaching onactual fuel used versus the fuel needed to get the job done. If thedriver follows the display, he or she saves fuel. The coach applicationautomatically measures, detects and calculates, in real-time orsubstantially real-time, an optimal setting for the vehicle throttlethat adapts to the particular driving job, regardless of vehicle classor load. The display is simple to use and provides a common andwell-known metaphor that does not distract the driver. By integratingthe application into a conventional smartphone or table, hardware costsfor the solution are minimal, and the approach is easy to integrate. Thecloud-based data services model enables fleet operators and others whouse the system easy and secure access to data, reports, leader-boards,or the like and improves analysis, tracking and reporting.

The approach enables users to save fuel. Visual (and, optionally, audioand/or haptic) cues provide real-time indicators that coach toward idealfuel performance regardless of truck class or load. The solution engagesand centers driver attention on the things that translate to superiorsafety performance. A by-product is fewer accidents and lower overalloperating costs. With the described approach, both drivers and fleetmanagers have detailed performance data at their fingertips. The datadrives win-win incentive plans and reward programs that help improvedriver retention.

The described technique (using the one or more algorithms) adapts tovarying roads, loads and other factors that are out of the drivers'control to set an efficient target for the driver to follow. Each tripis scored and logged to feed a monthly or quarterly performance-basedincentive program.

While the disclosed subject matter has been described in the context ofa method or process, the subject disclosure also relates to apparatusfor performing the operations herein. This apparatus may be speciallyconstructed for the required purposes, or it may comprise ageneral-purpose computer selectively activated or reconfigured by acomputer program stored in the computer. Such a computer program may bestored in a computer readable storage medium, such as, but is notlimited to, any type of disk including an optical disk, a CD-ROM, and amagnetic-optical disk, a read-only memory (ROM), a random access memory(RAM), a magnetic or optical card, or any type of media suitable forstoring electronic instructions, and each coupled to a computer systembus.

While given components of the system have been described separately, oneof ordinary skill will appreciate that some of the functions may becombined or shared in given instructions, program sequences, codeportions, and the like.

What is claimed is as follows.
 1. A system to assist an operator tocontrol a vehicle in a fuel-efficient manner, comprising: a throttle; amobile device having a display interface; and program code operative bya processor during operation of the vehicle for providing visualfeedback to the operator with respect to the operator's ability tomaintain an indicator in a given position on the display interface, theindicator representing a current fuel rate against a baseline associatedwith a set of zero acceleration fuel rate versus vehicle speed datapoints; the program code further operative to receive informationindicating a change in an amount of throttle applied by the operator,wherein the change in the amount of throttle adjusts fuel consumption bythe vehicle.